Gross primary production estimation by combining MODIS products and Ameriflux data through Artificial Neural Network for croplands

Vegetation productivity is the basis of all the biosphere activities on the land surface that relate to global biogeochemical cycles of carbon and nitrogen. The accurate quantification of gross primary production (GPP) in crops is important for regional and global studies of carbon budgets. Many flux observation nets have been established to help us monitoring the carbon cycling. However, estimation of GPP of terrestrial ecosystems for regions, continents, or the globe can improve our understanding of the feedbacks between the terrestrial biosphere and the atmosphere in the context of global change and facilitate climate policymaking. Remote sensing is a potentially powerful technology with which to extrapolate eddy covariance-based GPP to continental scales. In this paper, we combined MODIS products and Ameriflux networks data to simulate and predict GPP at four different cropland sites, using the Artificial Neural Networks (ANN). The results were quite approving compared to MODIS GPP product and tower-based measurements, which indicated it could be an applicable approach for GPP estimation.